For companies where cost of critical machine failure and operational downtime is a big risk, countermeasures to avoid downtime is crucial. In this regard, one of the best countermeasures to avoid costly downtime is predictive maintenance. Predictive maintenance offers a proactive approach to solving unplanned interruptions due to equipment or machine failure that can lead to costly downtime.
A photovoltaic (PV) solar plant supplies electricity through a complex combination of equipment, including many solar panels. The output of the plant is influenced by factors such as the weather and the direction of the sun. Failure of the equipment can happen on a small part of the large, complicated plant and can go unnoticed for a lengthy period of time. This, again, is where the benefit of predictive maintenance is evident – it has the ability to detect faults or potential breakages before the human eye detects it and, of course, before a costly breakdown occurs.
In this case, a photovoltaic (PV) solar plant supplies electricity through a complex combination of equipment, including many solar panels. Predictive maintenance aims to detect faults or potential breakages before the human eye detects it and, of course, before a costly breakdown occurs.
How Polymorph addresses the challenge with machine learning
Polymorph was granted access to the manufacturer’s existing and historical data to first analyse and then use to create and improve a machine-learning model that can detect operational anomalies and determine on a granular level when and exactly where certain equipment is starting to drift outside of its performance specifications which is usually indicative of an imminent breakdown.
Next, the machine learning model analyses all the data and determines if the machine or piece of equipment functions optimally. It learns from historical data and, over time, creates a trained model using and applying data such as the time of day, month, and year comparable to applied statistics.
Similar techniques are used in, for example, fraud detection on credit card transactions. The trained models can detect the fault quicker or identify a problem better than humans.
The Polymorph Platform
It is important to keep in mind that effective predictive maintenance requires accurate data. The data, however, is often found at various locations and not optimally utilised. A further important aspect to consider is cleaning the data and so exclude any unnecessary data that plays no role in improving and refining the machine learning model.
In terms of data storage, data is stored in a central cloud database making it easy and fast to extract and write to. Polymorph further created an easy to use user interface that can be built into intelligent dashboards. The next step in the process is to build a trained, healthy model that will be used to detect errors and abnormal behaviour. The aim of this model is for it to never stop learning. As it collects and processes increased data it becomes better trained to make better decisions.
Alerts and reporting
The Polymorph platform can easily integrate with business processes, create alarms and send emails and SMS’s to selected parties in case of an emergency or simply inform the maintenance team should a machine or piece of equipment need to be serviced.
Downtime as a result of machine failures can be more costly than the actual machines breaking and, as mentioned above, can cost companies millions. Predictive maintenance using advanced IOT and machine learning can prevent unwanted downtime and save organisations a lot of money. Machine learning is a cost-effective approach to predicting imminent failures and the maintenance cycles of machinery or equipment. This technique detects any deterioration or the onset of it in machinery that requires maintenance and it can aid solar energy providers to address any maintenance issues beforehand.